pybaselines.Baseline.brpls
- Baseline.brpls(data, lam=100000.0, diff_order=2, max_iter=50, tol=0.001, max_iter_2=50, tol_2=0.001, weights=None)[source]
Bayesian Reweighted Penalized Least Squares (BrPLS) baseline.
- Parameters:
- dataarray_like, shape (N,)
The y-values of the measured data, with N data points. Must not contain missing data (NaN) or Inf.
- lam
float, optional The smoothing parameter. Larger values will create smoother baselines. Default is 1e5.
- diff_order
int, optional The order of the differential matrix. Must be greater than 0. Default is 2 (second order differential matrix). Typical values are 2 or 1.
- max_iter
int, optional The max number of fit iterations. Default is 50.
- tol
float, optional The exit criteria. Default is 1e-3.
- max_iter_2
float, optional The number of iterations for updating the proportion of data occupied by peaks. Default is 50.
- tol_2
float, optional The exit criteria for the difference between the calculated proportion of data occupied by peaks. Default is 1e-3.
- weightsarray_like, shape (N,), optional
The weighting array. If None (default), then the initial weights will be an array with size equal to N and all values set to 1.
- Returns:
- baseline
numpy.ndarray, shape (N,) The calculated baseline.
- params
dict A dictionary with the following items:
- 'weights': numpy.ndarray, shape (N,)
The weight array used for fitting the data.
- 'tol_history': numpy.ndarray, shape (J, K)
An array containing the calculated tolerance values for each iteration of both threshold values and fit values. Index 0 are the tolerence values for the difference in the peak proportion, and indices >= 1 are the tolerance values for each fit. All values that were not used in fitting have values of 0. Shape J is 2 plus the number of iterations for the threshold to converge (related to max_iter_2, tol_2), and shape K is the maximum of the number of iterations for the threshold and the maximum number of iterations for all of the fits of the various threshold values (related to max_iter and tol).
- baseline
References
Wang, Q., et al. Spectral baseline estimation using penalized least squares with weights derived from the Bayesian method. Nuclear Science and Techniques, 2022, 140, 250-257.